I used the F5 TTS tool on Hugging Face and I generated 15 minutes of normal speech and trained it on Google Colab for 250 epochs.

Tip: Lower the transpose number and pitch for better accurate results.

Additional Details:

Tags: Frito-Lay, huggingface, ai-model

Related URL: https://huggingface.co/Ryanham1lton/ChesterCheetahJM/resolve/main/Chester%20Cheetah.zip?download=true

Download Link: https://huggingface.co/Ryanham1lton/ChesterCheetahJM/resolve/main/Chester%20Cheetah.zip?download=true Hosted on Hugging Face

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Voice accords

Main sonic traits

Selected-sample labels, grouped by what they describe.

Age

middle aged adult

young adult

elderly

Music use

producer tag

adlibs

speaking

Style/timbre

anime

villain

cute

Use case

advertisements

education and e-learning

podcasts

Sample deck

Lean and sweet spot

Pick a pitch, compare the Quality Score, then Run. Hold Ctrl or Cmd to select multiple pitches.

top 4.94 · avg 4.57

female

2

male

16

male base · pitch 0 Quality Score 4.94 · heard as male

Autorun

Run

male

-16 4.78 -12 4.74 -8 4.92 -4 4.93 0 4.94 4 4.84 8 4.73 12 4.57 16 4.54

female

-16 4.86 -12 4.85 -8 4.77 -4 4.76 0 4.75 4 4.53 8 4.21 12 3.21 16 3.35

heard female heard male Quality Score = estimated audio quality, higher is better Ctrl/Cmd-click = select multiple pitches

Similar voices

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Nearby voices from generated samples. Pick a pitch in the Sample Deck to refresh the matches.

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